Sustainable heat extraction from abandoned mine tunnels: A numerical model
Bibliographic record
Abstract
Abandoned mines are often associated with enduring liabilities, which involve significant costs for decades after the decommissioning of the mine. Using a decommissioned mine as a geothermal resource can offset the environmental costs by supplying green heat to the communities living in and around the mine area. In this paper, a numerical assessment of geothermal heat extraction from underground mine workings using an open loop geothermal system is carried out. In this study, our focus is on fully flooded mines where the heat flow from the rock mass to the mine cavities is dominantly controlled by conduction in the rock mass. The sustainable heat flux into the mine workings is assessed using a transient two-dimensional axisymmetric heat transfer model. Finite volume method is applied to solve the model and simulate the transient temperature fields in the rock mass and within the water (flowing through cavities). The model is capable of controlling the rate of heat extraction through continuous adjustment of the rate of water flow through the mine. Sustainable rate of heat extraction is calculated for seasonally varied heat loads and for different project life cycles. It is shown that, with proper resource management, each kilometre of a typical deep underground mine tunnel, can produce about 150 kW of usable heat in a sustainable manner. The model is validated by comparing its results with other published models and realistic data available from Springhill mine, Nova Scotia, Canada. It is found that the sustainable heat extraction is controlled dominantly by virgin rock temperature, thermal conductivity of the rock mass, and seasonal heat load variations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".